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For a given software bug report, identifying an appropriate developer whocould potentially fix the bug is the primary task of a bug triaging process. Abug title (summary) and a detailed description is present in most of the bugtracking systems. Automatic bug triaging algorithm can be formulated as aclassification problem, with the bug title and description as the input,mapping it to one of the available developers (classes). The major challenge isthat the bug description usually contains a combination of free unstructuredtext, code snippets, and stack trace making the input data noisy. The existingbag-of-words (BOW) feature models do not consider the syntactical andsequential word information available in the unstructured text. We propose anovel bug report representation algorithm using an attention based deepbidirectional recurrent neural network (DBRNN-A) model that learns a syntacticand semantic feature from long word sequences in an unsupervised manner.Instead of BOW features, the DBRNN-A based bug representation is then used fortraining the classifier. Using an attention mechanism enables the model tolearn the context representation over a long word sequence, as in a bug report.To provide a large amount of data to learn the feature learning model, theunfixed bug reports (~70% bugs in an open source bug tracking system) areleveraged, which were completely ignored in the previous studies. Anothercontribution is to make this research reproducible by making the source codeavailable and creating a public benchmark dataset of bug reports from threeopen source bug tracking system: Google Chromium (383,104 bug reports), MozillaCore (314,388 bug reports), and Mozilla Firefox (162,307 bug reports).Experimentally we compare our approach with BOW model and machine learningapproaches and observe that DBRNN-A provides a higher rank-10 average accuracy.

DeepTriage: Exploring the Effectiveness of Deep Learning for Bug Triaging